Has anyone of you out there ever tried to build a robot inteligence based on neural networks? Im pretty sure many of you have tried the "bottom-up" aproch to robot intelignce as described by Rodney Brooks, in one way or another. Ive seen many atempts to do this, but i have not been able to find any amateur projects that realy utilize the full power of deisigning a subsumption architecture behavior level based robot AI based on a neural network.
Im currently trying this aproch myself, implementing a neural network in software in a PIC18F452. The way ive decided to implement the neurons is as a combination of data structs and a state machine function (one for each neuron). The structs hold the input and output data to each AFSM(augmented finite state machine). The function the handles all the states and conditions of state chaning in order to operate on the data of the neuron. The functions then has to be run in paralel, starting at level 0 and going upwards. The inhibit effect is done thru letting the higher level neurons overwrite the lower levels (similar to what Devantech does in their example robot "Chucky").
So far this aproch seems to work very well. With adding alitle convention to the way variables are named its quite easy to keep track of what data is inputs, what is outputs (what variables should be read and what should be written, its important to keep those apart).
If anyone have ever done anything along this path, it would be intresting to hear your thouights on the thing.
Im basing my design on Brooks own design ideas behind his hexapod "Ghenigs" , as discribed in an article i found in his book "Cambrain Intelignce"(highly recomend this book to anyone who is serius about robot AI).